import pandas as pd
import numpy as np

# Load the dataset
df = pd.read_csv('raw_ecommerce_data.csv')

# Initial data preview
print("Initial Dataset:")
print(df.head())

Initial Dataset:
   Order ID Customer Name      Gender         Category  Quantity   Price  \
0  ORD-1000    Customer 0        Male      Electronics         6  363.75   
1  ORD-1001    Customer 1        Male  Health & Beauty         8     NaN   
2  ORD-1002    Customer 2      Female            Books         8  452.38   
3  ORD-1003    Customer 3  Non-Binary      Electronics         8  437.44   
4  ORD-1004    Customer 4      Female  Health & Beauty         1  353.61   

        State Payment Method Purchase Date    Total  
0  California           Cash    2023-01-01  2182.50  
1    New York         PayPal    2023-01-02      NaN  
2    New York         PayPal    2023-01-03  3619.04  
3    New York         PayPal    2023-01-04  3499.52  
4    New York    Credit Card    2023-01-05   353.61

# Check for missing values
print("\nMissing Values:")
print(df.isnull().sum())

Missing Values:
Order ID           0
Customer Name     12
Gender             0
Category           0
Quantity           0
Price             11
State              0
Payment Method     0
Purchase Date      0
Total             11
dtype: int64

# Fill missing Customer Names with "Anonymous"
df['Customer Name'] = df['Customer Name'].fillna('Anonymous')

df.head()

df.tail()

df

# Fill missing Prices with the mean price
mean_price = df['Price'].mean()
df['Price'] = df['Price'].fillna(mean_price)

df

print(df.isnull().sum())

Order ID           0
Customer Name      0
Gender             0
Category           0
Quantity           0
Price              0
State              0
Payment Method     0
Purchase Date      0
Total             11
dtype: int64

# Recalculate Total
df['Total'] = df['Quantity'] * df['Price']

df.head()

# Replace 'Unknown' in Category with 'Miscellaneous'
df['Category'] = df['Category'].replace('Unknown', 'Miscellaneous')

# Convert Purchase Date to datetime
df['Purchase Date'] = pd.to_datetime(df['Purchase Date'])

df.head()

# Check cleaned data
print("\nCleaned Dataset:")
print(df.info())

Cleaned Dataset:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 10 columns):
 #   Column          Non-Null Count  Dtype         
---  ------          --------------  -----         
 0   Order ID        100 non-null    object        
 1   Customer Name   100 non-null    object        
 2   Gender          100 non-null    object        
 3   Category        100 non-null    object        
 4   Quantity        100 non-null    int64         
 5   Price           100 non-null    float64       
 6   State           100 non-null    object        
 7   Payment Method  100 non-null    object        
 8   Purchase Date   100 non-null    datetime64[ns]
 9   Total           100 non-null    float64       
dtypes: datetime64[ns](1), float64(2), int64(1), object(6)
memory usage: 7.9+ KB
None

# Save the cleaned dataset
cleaned_csv_path = 'cleaned_ecommerce_data.csv'
df.to_csv(cleaned_csv_path, index=False)
print(f"Cleaned dataset saved: {cleaned_csv_path}")

Cleaned dataset saved: cleaned_ecommerce_data.csv

import matplotlib.pyplot as plt
import seaborn as sns

# Set visualization style
sns.set_style('whitegrid')

# Top Categories by Revenue
category_revenue = df.groupby('Category')['Total'].sum().sort_values(ascending=False)

plt.figure(figsize=(10, 6))

<Figure size 1000x600 with 0 Axes>

<Figure size 1000x600 with 0 Axes>

sns.barplot(x=category_revenue.index, y=category_revenue.values, palette='viridis')

C:\Users\switc\AppData\Local\Temp\ipykernel_17820\3810512292.py:1: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=category_revenue.index, y=category_revenue.values, palette='viridis')

<Axes: xlabel='Category'>

plt.figure(figsize=(10, 6))
sns.barplot(x=category_revenue.index, y=category_revenue.values, palette='viridis')
plt.title('Top Categories by Revenue', fontsize=16)
plt.xlabel('Category', fontsize=12)
plt.ylabel('Revenue ($)', fontsize=12)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

C:\Users\switc\AppData\Local\Temp\ipykernel_17820\3870076080.py:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=category_revenue.index, y=category_revenue.values, palette='viridis')

# Sales Trend Over Time
sales_trend = df.groupby('Purchase Date')['Total'].sum()

plt.figure(figsize=(12, 6))
plt.plot(sales_trend.index, sales_trend.values, marker='o', color='dodgerblue')
plt.title('Sales Trend Over Time', fontsize=16)
plt.xlabel('Date', fontsize=12)
plt.ylabel('Total Sales ($)', fontsize=12)
plt.grid(True)
plt.tight_layout()
plt.savefig('sales_trend.png')
plt.show()

import plotly.express as px

# Payment Method Distribution
payment_counts = df['Payment Method'].value_counts()

fig = px.pie(
    values=payment_counts.values,
    names=payment_counts.index,
    title='Payment Method Distribution',
    color_discrete_sequence=px.colors.sequential.RdBu
)

fig.show()

	Order ID	Customer Name	Gender	Category	Quantity	Price	State	Payment Method	Purchase Date	Total
95	ORD-1095	Customer 95	Non-Binary	Health & Beauty	5	348.53	Florida	PayPal	2023-04-06	1742.65
96	ORD-1096	Customer 96	Non-Binary	Home Decor	6	247.83	Florida	Debit Card	2023-04-07	1486.98
97	ORD-1097	Customer 97	Non-Binary	Health & Beauty	6	204.74	California	Cash	2023-04-08	1228.44
98	ORD-1098	Customer 98	Female	Electronics	4	275.52	Texas	Debit Card	2023-04-09	1102.08
99	ORD-1099	Customer 99	Female	Clothing	6	20.89	New York	Cash	2023-04-10	125.34

	Order ID	Customer Name	Gender	Category	Quantity	Price	State	Payment Method	Purchase Date	Total
0	ORD-1000	Customer 0	Male	Electronics	6	363.75	California	Cash	2023-01-01	2182.50
1	ORD-1001	Customer 1	Male	Health & Beauty	8	NaN	New York	PayPal	2023-01-02	NaN
2	ORD-1002	Customer 2	Female	Books	8	452.38	New York	PayPal	2023-01-03	3619.04
3	ORD-1003	Customer 3	Non-Binary	Electronics	8	437.44	New York	PayPal	2023-01-04	3499.52
4	ORD-1004	Customer 4	Female	Health & Beauty	1	353.61	New York	Credit Card	2023-01-05	353.61